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Jurnal Elkasista
Published by Politeknik Angkatan Darat
ISSN : -     EISSN : 27231291     DOI : -
Core Subject : Science, Engineering,
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Materials Science & Nanotechnology
Jurnal Elkasista Poltekad membahas tentang keilmuan yang berhubungan dengan sensor transducer, sistem kontrol, artificial intelligent, sistem rangkaian analog digital, micro controller dan interfacing.
Arjuna Subject : Teknik (semua kategori) - Teknik Listrik dan Elektro
Articles 123 Documents
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Analysis of Battery Charging Using 100 Wp Solar Cells as Power Supply for Water Desalination Equipment: Teknologi kurniawan, jefri
Elektronika Sistem Senjata Vol 5 No 2 (2024): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v5i2.533

Abstract

This study analyzed battery charging using a 100 Wp solar cell as apower source in a water desalination device. The main goal is to find out theamount of energy produced by the 100 W solar cell and the optimal batterycharging time. The research method involves the use of various components suchas solar charge controllers, 12-volt batteries, and inverters. The results showedthat the energy capacity stored in the battery reached 768 Wh, with the highestcharging efficiency on the fourth day of 137 Wh. Battery charging is affected by theintensity of sunlight, weather conditions, and the working position of the solar cell.Tests also show that the electrical energy generated can support the desalinationdevice for 10 hours with varying filling efficiency due to environmental factors. Thisresearch provides insight into the efficiency of solar power systems in varyingweather conditions and their practical applications in water desalination.
PENGAPLIKASIAN DEEP LEARNING DENGAN DESAIN ARSITEKTUR JST UNTUK TUGAS REGRESI DAN KLASIFIKASI: Elektronika lesmana, dadang
Elektronika Sistem Senjata Vol 6 No 1 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i1.564

Abstract

Abstract: Technological developments have driven the application of Deep Learning, particularly Artificial Neural Networks (ANN), in solving regression and classification tasks. ANNs consist of interconnected artificial neurons that are effective in classification, prediction, and pattern recognition. This study aims to build and analyze the impact of variations in Artificial Neural Network (ANN) architecture on prediction accuracy, using a quantitative experimental method with a controlled randomized design. The BostonHousing.csv dataset was used for regression, and the Iris.csv dataset for classification. Regression evaluation uses MSE, R², and MAE; while classification uses accuracy and confusion matrix. The best regression results were obtained from the 4-hidden-layer architecture (512, 256, 128, 64), with ReLU, sigmoid, tanh, and ReLU activation functions, a learning rate of 0.001, achieving an R² of 89.2% and an MAE of 1.94. For classification, the best architecture (128, 64, 32, 16) with a softmax output yielded an accuracy of 99.8% and a model accuracy of 100%. Keywords – deep learning, artificial neural network, regression, classification, ANN architecture
IMPLEMENTATION OF IOT-BASED SMART FARMING USING MQTT PROTOCOL: Elektronika Indra, Johan; Eka, Vanka; Widyatmoko, Dekki
Elektronika Sistem Senjata Vol 6 No 2 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i2.573

Abstract

This research aims to implement smart farming solutions using the MQTT protocol within the framework of the Internet of Things (IoT). The use of IoT technology is expected to increase agricultural productivity and efficiency through better monitoring and control of crop conditions. Analysis, system design, tool development, and functionality testing are the methods used in this research. The results show that the developed system is capable of real-time monitoring of environmental parameters such as soil moisture, temperature, and lighting. This system can provide notifications to users through the red node dashboard regarding plant conditions and maintenance needs such as watering using integrated sensors and MQTT-based communication. This system can also use soil moisture sensor data as an indicator to manually control the water pump actuator at the water source by the user according to the soil moisture status. This research concludes that the implementation of IoT-based smart farming with MQTT protocol can improve efficiency in agricultural management, provide accurate and real-time information for better decision-making, and support the sustainability of agricultural production in the digital era.
IMPLEMENTATION OF DEEP LEARNING WITH ARTIFICIAL NEURAL NETWORK ARCHITECTURE FOR IMAGE CLASSIFICATION USING AUTOENCODER TECHNIQUE ahmad, elit
Elektronika Sistem Senjata Vol 6 No 1 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i1.574

Abstract

Digital image processing is rapidly evolving along with advances in artificial intelligence technology, particularly in the field of deep learning. In this context, the use of Artificial Neural Network (ANN) architecture has proven effective in improving image classification performance. The main objective of this study is to integrate autoencoder techniques into the ANN structure to improve accuracy in the image classification process. Autoencoders, which are unsupervised learning methods, function to extract important and representative features from a given image. These features are then used as input for the classification layer in a neural network. In this experiment, a carefully curated image dataset was used to train the model. After training, the model was tested and evaluated based on several performance metrics, including accuracy, precision, and recall. The test results significantly showed that the addition of autoencoders in the ANN architecture provided a significant increase in classification accuracy compared to conventional approaches that did not use this technique. These findings prove that autoencoders can play a significant role in improving the quality of deep learning-based classification systems, especially in applications that require more accurate and efficient image analysis.
IMPLEMENTATION OF RGB COLOR SEGMENTATION SYSTEM ON DIGITAL IMAGERY FOR OCTAVE-BASED COLOR DOMINANCE CLASSIFICATION: Elkasista Dwi , Hendro; Maulana, Irfan Maulana; Prabowo, Choirul RiO
Elektronika Sistem Senjata Vol 6 No 2 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i2.580

Abstract

Abstract: A digital color image consists of three main components, namely red, green, and blue, known as the RGB color model. This study aims to implement a color segmentation method on digital images to classify color dominance using Octave application. The segmentation process is carried out by comparing the intensity of each color channel and producing three output images: red-dominant, green-dominant, and blue-dominant regions. The image was used for testing the effectiveness of color separation through logical masking. The results show that this method accurately isolates objects based on dominant colors and can be applied to various image processing tasks such as object tracking, color classification, and visual analysis. This research provides a foundation for developing simple yet effective computer vision systems
ANALYSIS OF INFRARED SENSOR IMPLEMENTATION IN LINE FOLLOWER ROBOT A LITERATURE REVIEW OF DETECTION METHODS AND CONTROL ALGORITHMS Topik, Topik; Widyatmoko, Dekki; Pranoto, Eko
Elektronika Sistem Senjata Vol 6 No 2 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i2.615

Abstract

The development of robotics technology shows significant evolution in the implementation of infrared sensors in line follower robots that integrate advanced detection methods and sophisticated control algorithms. This research aims to comprehensively analyse the implementation of infrared sensors through a structured literature review of eight major studies in the 2021-2025 range. A library research methodology was used with thematic analysis to categorise the findings based on sensor type, control algorithm, and performance metrics. The analysis shows the transformation from a single infrared sensor to a hybrid multi-modal system that integrates computer vision, ultrasonic, and colour sensors with improved detection accuracy, reaching an error rate of 0.1-85 pixels. Diversification of control algorithms from classical PID to fuzzy logic, neural network, and Q-learning proved superior adaptability to a dynamic environment with 66.67%-100% success rate. The convergence of wireless communication technology and autonomous learning capabilities marks the transition of the line follower robot into a platform for automated guided vehicles and autonomous mobile robots. The conclusion shows that infrared sensors maintain high relevance in modern robotics when integrated with advanced computational methods, providing a foundation for the development of intelligent and adaptive navigation systems for the implementation of Industry 4.0.
Teknologi IMPLEMENTATION OF ENEMY IDENTIFICATION SYSTEM USING IMAGE PROCESSING TECHNOLOGY AUTOMATIC SECURITY ROBOT: Elektronika Maulana, Irfan; Wijaya, Bintar; Dwi, Hendro
Elektronika Sistem Senjata Vol 6 No 2 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i2.624

Abstract

The Industrial Revolution brought significant advances in military technology, one of which was the development of a robotic system to improve security. Indonesia, particularly Papua, still faces armed threats. This study designed and implemented an image processing-based SS7 shield guard robot prototype to distinguish between friend and foe. The system uses a camera as a visual sensor, Raspberry Pi as a transmission center, and wireless data transmission to the guard post in real time. Tests were conducted at varying distances (2-16 meters), viewing angles (0°–90°), and light intensity (0-maximum Lux). The best results were obtained at a distance of 2-12 meters, an angle of 0°-60°, and a minimum illumination of 169 lux, with stable detection accuracy across a wide range of objects. The system demonstrated optimal performance in bright conditions and decreased at 0 Lux illumination. These findings demonstrate that the integration of hardware and image processing algorithms can support improved security for military posts and serve as an initial step towards the development of more adaptive automated security systems in the future.
ANALYSIS OF THE KALMAN FILTER METHOD ON A GYROSCOPE TO REDUCE NOISE TO IMPROVE RESPONSIBILITY IN A SHOOTING Widiatmoko, Dekki; Eka Setiawan, Rian Putra; Hairani, Hairani; utomo, rokhim
Elektronika Sistem Senjata Vol 6 No 2 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i2.671

Abstract

This study aims to analysis how effective the Kalman Filter method is in reducing noise in gyroscope signals to improve the responsiveness of a shooting simulator. By using the Kalman Filter, the data from the gyroscope becomes more accurate, leading to a more realistic shooting simulation experience. The clearer signal not only improves orientation accuracy but also reduces the system's response time, making the simulator faster and more precise in reacting to user inputs. The results show that the Kalman Filter significantly enhances the performance of the shooting simulator, which is crucial in military and security settings where accuracy and quick response are essential.
TIMING SYSTEM FOR STUDENT NCO ACTIVITIES OF POLTEKAD USING ATMEGA328 BASED RTC Widiatmoko, Dekki; Wicaksono, Agung Tri; Walid, Miftahul; Eka Setiawan, Rian Putra
Elektronika Sistem Senjata Vol 6 No 2 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i2.672

Abstract

This research aims to develop an activity time marking system specifically designed for the daily activities of Poltekad Student Non-Commissioned Officers such as prayer times, meal times, sports roll call times and checking roll times, using an ATmega328 microcontroller. This system will use an RTC (Real-Time Clock) module to maintain accurate time and provide notifications via sound notification or alarm. By utilizing assistive technology, this system will help users remember and organize their various daily activities. Microcontroller programming and hardware interfacing are an integral part of the development of this system, which is expected to increase accessibility and timeliness when carrying out an activity.
Implementation of a Thermoelectric Generator Series Circuit to Increase the Output Voltage by 5V as a HT charger to support TNI duties Kasiyanto, Kasiyanto; Fauzi Wibisono, Wahyu Imam; Sridaryono, Aguk; Arianto, Yuri
Elektronika Sistem Senjata Vol 6 No 2 (2025): Jurnal Elkasista
Publisher : Pustaka Poltekad

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54317/elka.v6i2.673

Abstract

This research aims to implement a TEG series circuit to achieve an output voltage that exceeds 5V, so that it can charge the HT. The method used in this research involves configuring several TEG modules in series to increase the overall voltage. This research begins with selecting an appropriate TEG and arranging it into a series circuit, followed by testing to measure the output voltage and system efficiency at variable temperatures. Trials also prove that increasing the module in a series circuit can significantly increase the output voltage, but temperature and electrical stability require special attention to improve equipment performance. The conclusion of this research is that the TEG series circuit is more effective in increasing the output voltage for HT charging. with results according to the desired target. Suggestions for further research include exploring the use of the TEG module to get maximum results, as well as developing a better temperature management system to optimize performance in real conditions. This research provides a clear basis for the development of TEG-based charging technology in other applications that require high voltage.

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